Scalable customization of atrial fibrillation detection in cardiac monitoring devices: Increasing detection accuracy through personalized monitoring in large patient populations

Kuk Jin Jang, Guha Balakrishnan, Zeeshan Syed, Naveen Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

To make it viable for remote monitoring to scale to large patient populations, the accuracy of detectors used to identify patient states of interests must improve. Patient-specific detectors hold the promise of higher accuracy than generic detectors, but the need to train these detectors individually for each patient using expert labeled data limits their scalability. We explore a solution to this challenge in the context of atrial fibrillation (AF) detection. Using patient recordings from the MIT-BIH AF database, we demonstrate the importance of patient specificity and present a scalable method of constructing a personalized detector based on active learning. Using a generic detector having a sensitivity of 76% and a specificity of 57% as its seed, our active learning approach constructs a detector with a sensitivity of 90% and specificity of 85%. This performance approaches that of a patient-specific detector, which has a sensitivity of 94% and specificity of 85%. By selectively choosing examples for training, the active learning approach reduces the amount of expert labeling needed by almost eight fold (compared to the patient-specific detector) while achieving accuracy within 99%.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages2184-2187
Number of pages4
DOIs
StatePublished - Dec 26 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'Scalable customization of atrial fibrillation detection in cardiac monitoring devices: Increasing detection accuracy through personalized monitoring in large patient populations'. Together they form a unique fingerprint.

  • Cite this

    Jang, K. J., Balakrishnan, G., Syed, Z., & Verma, N. (2011). Scalable customization of atrial fibrillation detection in cardiac monitoring devices: Increasing detection accuracy through personalized monitoring in large patient populations. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 2184-2187). [6090411] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6090411